Non-Iterative, Feature-Preserving Mesh Smoothing

dc.creator	Jones, Thouis R.
dc.creator	Durand, Frédo
dc.creator	Desbrun, Mathieu
dc.date	2003-12-13T19:39:26Z
dc.date	2003-12-13T19:39:26Z
dc.date	2004-01
dc.date.accessioned	2013-10-09T02:32:53Z
dc.date.available	2013-10-09T02:32:53Z
dc.date.issued	2013-10-09
dc.identifier	http://hdl.handle.net/1721.1/3866
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721
dc.description	With the increasing use of geometry scanners to create 3D models, there is a rising need for fast and robust mesh smoothing to remove inevitable noise in the measurements. While most previous work has favored diffusion-based iterative techniques for feature-preserving smoothing, we propose a radically different approach, based on robust statistics and local first-order predictors of the surface. The robustness of our local estimates allows us to derive a non-iterative feature-preserving filtering technique applicable to arbitrary "triangle soups". We demonstrate its simplicity of implementation and its efficiency, which make it an excellent solution for smoothing large, noisy, and non-manifold meshes.
dc.description	Singapore-MIT Alliance (SMA)
dc.format	8331712 bytes
dc.format	application/pdf
dc.language	en_US
dc.relation	Computer Science (CS);
dc.subject	mesh smoothing
dc.subject	robust statistics
dc.subject	mollification
dc.subject	feature preservation
dc.title	Non-Iterative, Feature-Preserving Mesh Smoothing
dc.type	Article

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